TY - JOUR AU - Korporaal, Michelle AU - Ruginski, Ian T. AU - Fabrikant, Sara Irina PY - 2020 M3 - Original Research TI - Effects of Uncertainty Visualization on Map-Based Decision Making Under Time Pressure JO - Frontiers in Computer Science UR - https://www.frontiersin.org/articles/10.3389/fcomp.2020.00032 VL - 2 SN - 2624-9898 N2 - Most of our daily activities in a highly mobile digital society require timely spatial decision-making. Much of such decision-making is supported by map displays on various devices with different modalities. Spatial information visualized on maps, however, is always subject to a multitude of uncertainties. If space-time decision-makers are not informed about potential uncertainties, misleading, or at worst, life-threatening outcomes might result from map-based decisions. Therefore, data uncertainties should be communicated to decision-makers, especially when they are made with limited time resources and when decision outcomes can have dramatic consequences. Thus, the current study investigates how data uncertainty visualized in maps might influence the process and outcomes of spatial decision-making, especially when made under time pressure in risky situations. Although there is very little empirical evidence from prior uncertainty visualization research that considered decision time constraints, we hypothesized that uncertainty visualization would also have an effect on decision-making under time critical and complex decision contexts. Using a map-based helicopter landing scenario in mountainous terrain, we found that neither time pressure nor uncertainty affected participants decision-making accuracy. However, uncertainty affected participants' decision strategies, and time pressure affected participants' response times. Specifically, when presented with two equally correct answers, participants avoided uncertainty more often at a cost of landing distance (an equally important decision criteria). We interpret our results as consistent with a loss-aversion heuristic and suggest implications for the study of decision-making with uncertainty visualizations. ER -